Artificial intelligence has become a daily companion for many software architects. In 2025, it is less about “choosing the best AI tool” and more about curating and evolving a living toolset. No single platform covers all needs - at least not yet. Instead, we must constantly experiment, combine, and adapt AI capabilities to support architecture design, technical leadership, and delivery in ever-changing environments.
This post outlines how we use AI tools in our practice, along with examples of how they support the software architect role.
The Architect’s Role and AI Support
Software architects are expected to:
- Shape system design by balancing business requirements, constraints, and technical trade-offs.
- Guide teams in applying architectural principles and patterns across distributed, cloud-native, and DevOps contexts.
- Bridge communication between engineering, leadership, and business stakeholders.
AI tools do not replace these responsibilities. Instead, they provide acceleration, augmentation, and reflection:
- Acceleration: speeding up research, prototyping, and documentation.
- Augmentation: extending reasoning, generating options, and exploring trade-offs.
- Reflection: providing different perspectives on decisions and assumptions.
Categories of AI Tools for Architects
1. Searching & Learning
- Perplexity.ai – quick search across sources, exploring new technologies, and summarizing new research.
- Useful for: learning about the latest AWS/Azure/GCP services, validating assumptions against documentation, forums, and the broader web.
2. Brainstorming & Ideation
- ChatGPT (GPT-5) – rapid brainstorming, generating scenarios, and “talking through” design alternatives.
- Useful for: early-stage thinking, exploring edge cases, and facilitating design discussions.
3. Prompt Engineering
- GitHub Copilot – helps refine prompts and supports small-scale refactoring and documentation.
- OpenAI Prompt Optimizer (GPT-5) – supports improving prompt clarity, efficiency, and outcome quality.
- Useful for: creating repeatable, reliable prompts that make AI collaboration more effective across tasks.
4. Agentic Coding & Prototyping
- Claude Code with subagents – supports coding, proof-of-concept implementations, and technical design, including drafting Architecture Decision Records (ADRs). Example subagents:
- Domain Driven Design Expert - focuses on discovering and identifying subdomains, categorizing them as core, supporting, or generic, and drafting bounded contexts.
- Code Reviewer (specialized for specific languages or frameworks) - reviews code for best practices, potential bugs, and adherence to coding standards, may use linters and static analysis tools.
- Trade-off Analyst - evaluates different architectural options based on criteria such as performance, scalability, cost, and maintainability, providing a balanced view of pros and cons.
- Useful for: almost everything from quick prototyping to detailed design documentation. The key is to leverage subagents for specialized tasks and narrow prompts context to the specific challenge.
- Integrated MCPs (Model Context Protocol servers):
- Serena for querying the codebase (works especially well with bigger codebases).
- Context7 for browsing documentation.
- Sequential Thinking for structured AI planning.
Together, these tools provide architecture-aware prototyping and knowledge-driven design workflows, connecting documentation, code, and work planning into a coherent flow.
List to Explore Next
The landscape evolves quickly. Here are tools we plan to explore next:
- OpenAI Codex – potential alternative to Claude Code.
- Warp – positioning itself as an “Agentic Development Environment.” Worth comparing with Claude Code and Codex.
- Firecrawl MCP – expanding external data integration for agent workflows.
- Parallel agent workflows – experimenting with multiple agents working on subtasks simultaneously.
This exploration list serves as a reminder: an architect’s AI toolset is never static. Staying curious and testing new capabilities is part of the job.
Practical Applications in Daily Work
Here is how these categories play out in real projects:
- Architecture Exploration: Use Perplexity to scan for recent practices or solutions (e.g., managed database services in AWS) or quickly validate knowledge (e.g., the
TESTCONTAINERS_RYUK_DISABLEDflag), and ChatGPT to brainstorm architectural options before deeper design sessions. - Prompt Quality: Refine prompts with Copilot or the GPT-5 Prompt Optimizer to achieve more consistent outcomes when generating documents or test suites.
- Rapid Prototyping: Pair Claude Code with MCPs to validate feasibility through quick proofs of concept, then decide if the pattern is viable for production.
- Documentation at Scale: Generate ADR drafts and architecture overviews with Claude Code (especially using dedicated subagents who are experts in trade-off analysis).
- Planning as first-class work: Break down complex tasks into manageable steps, then use agents to work through them iteratively, ensuring alignment with architectural goals.
- Best engineering practices matter more than ever: AI tools can introduce errors or misinterpretations. When working with AI-generated code, practices such as linting, formatting, maintaining test coverage, conducting code reviews, and enforcing CI become even more critical.
Keep the Toolset Alive
In 2025, there is no perfect or final AI toolset for software architects. Instead, the practice is about:
- Continuous evolution: adopt, test, and discard tools as needs change.
- Balancing categories: ensure coverage across searching, brainstorming, prompting, coding, and documentation.
- Architect mindset first: tools augment judgment; they do not replace it.
The most effective architects will not only master today’s AI assistants but also develop an experimentation habit - treating AI tools as evolving collaborators in the craft of system design.
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